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Activity Number: 107 - SPEED: Statistical Methods, Computing, and Applications Part 1
Type: Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #322533
Title: Developing Logistic Regression for the High-Dimensional DNA Methylation Data
Author(s): Mohamed salem Milad*
Companies: Arkansas State University
Keywords: Logistic Regression ; DNA methylation
Abstract:

DNA methylation is an epigenetic modification that can alter gene expression without a DNA sequence change. Many statistical methods have been developed to test for a difference in DNA methylation levels between groups (e.g. healthy vs disease) at individual cytosines. Site level testing is often followed by a post hoc aggregation procedure that explores regional differences. Although analyzing CpGs individually provides useful information, there are both biological and statistical reasons to test entire genomic regions for differential methylation. The individual loci may be noisy but the overall regions tend to be informative. Also, the biological function of regions is better studied and are more correlated to gene expression, so the interpretation of results will be more meaningful for region-level tests. This study focuses on developing logistics regression to identify differentially methylated regions (DMRs) that will enable discovery of epigenomic structural variations in NGS data. Using simulated data, the performance of the novel approaches is compared with an alternative method (M3D) for region level testing


Authors who are presenting talks have a * after their name.

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